93 results about "Mixed model" patented technology

The invention discloses an automobile seat framework machining defect detection method based on machine vision, and particularly relates to the technical field of defect detection. The method comprises the following steps: constructing a multi-angle image acquisition and edge reflection modeling module aiming at complex defects such as weld joint pseudo soldering, microcracks, hole site deviation and collapse deformation, extracting weld joint continuity, edge integrity and hole site geometric consistency characteristics by using a deep neural network, and generating a structural feature vector; defect type recognition and credibility scoring are completed through small sample anomaly modeling and Gaussian mixture model classification, sub-pixel-level coordinate labeling of defect positions is achieved in combination with a Gaussian fitting algorithm, a defect positioning map is output, traceability analysis and severity grading are conducted based on historical data comparison, and the defect positioning accuracy is improved. The method is suitable for industrial online detection and quality closed-loop control.

The invention discloses an interlayer gold purity evaluation method and device based on a hybrid model, equipment and a medium. The method comprises the following steps: collecting original data; inputting the processed original data into a hybrid model for training to obtain a trained hybrid model; wherein the hybrid model comprises a feature extraction module, a weighted fusion unit and a classification module which are connected in sequence; collecting a second pulsed eddy current signal and a second supplementary feature corresponding to the gold to be detected; inputting the second pulse eddy current signal and the second supplementary feature into a trained hybrid model, and outputting a prediction vector; and converting the prediction vector into a one-hot code, and obtaining the number of the corresponding gold to be tested through the one-hot code. The problem that an existing classification mixing model cannot be suitable for a highly-adulterated gold purity detection scene is solved, the classification precision of adulterated gold purity detection is improved, and the application range of the classification mixing model in the field of scarce sample detection is widened.

The invention discloses a main beam formwork erection elevation adjustment method based on machine learning, and belongs to the technical field of elevation adjustment. The method comprises the steps that a space-time database is constructed by collecting data of the construction period of a cable-free section; processing the time sequence data by adopting an LSTM-Transform hybrid model, extracting long-period characteristics, and outputting an initial prediction value of the elevation; analyzing nonlinear variables such as a cable force change rate, a sunlight gradient and a material age by using an XG-Boost algorithm, and outputting a compensation factor; fusing and generating a joint prediction value; constructing a reinforcement learning agent, and outputting an adjustment instruction by taking a construction stage as a state space, taking an elevation adjustment amount as an action space, minimizing deviation between a predicted value and a measured value and taking construction stability as a reward function; the formwork erecting elevation is adjusted according to the driving hydraulic system, and the database is updated in real time. According to the invention, through multi-dimensional data fusion and intelligent optimization, the elevation adjustment precision and stability are improved.

The invention relates to the technical field of electronics, and discloses an urban river pollutant traceability method and device, electronic equipment, a medium and a product. The method comprises the steps that when it is monitored that target pollutants of any target monitoring section exceed the standard, the target monitoring section serves as an end point; reversely simulating an upstream diffusion path of the target pollutants by using a pre-constructed mechanism and data hybrid model to obtain theoretical concentrations of the target pollutants in a plurality of upstream river sections; determining at least one traceable river reach in the plurality of upstream river reaches based on the theoretical concentration of the target pollutants; pollution characteristic data of a water body in the traceable river reach is obtained; performing similarity calculation on the pollution characteristic data and characteristic data of a plurality of pollution sources in a pre-constructed pollution source characteristic knowledge graph to obtain a similarity value between the pollution characteristic data and each pollution source; and determining at least one target pollution source in the plurality of pollution sources based on the similarity value, thereby realizing dual precision of qualitative and quantitative analysis of the pollution sources, and greatly improving the tracing efficiency.

The invention relates to the technical field of machine vision and optical measurement mechanics, and discloses a deformation identification and measurement method based on machine vision and deep learning, and the method comprises the following steps: S1, system calibration and base library construction; s2, offline training of the hybrid model; s3, real-time decoupling and coefficient regression are carried out; and S4, final assembly and physical quantity calculation of the deformation field. According to the method, a deformation field is decoupled into a global nonlinear deformation field () and a residual deformation field () through S3, and the global nonlinear deformation field () and the residual deformation field () are superposed. The physical significance and the stability of a deformation main body are ensured by utilizing the physical deformation base library constructed in S1 and model reconstruction; meanwhile, a luminosity residual image () generated in the S3 is used for driving module reconstruction, local high-frequency disturbance which is not covered by a physical model is accurately compensated, a high-dimensional deformation field is decoupled into low-dimensional global physical prior regression and residual correction, and efficient real-time measurement is achieved; meanwhile, numerical difference is replaced by analytic derivation, so that the signal-to-noise ratio and the measurement precision of the strain field are remarkably improved.

The invention provides a real-time time sequence prediction system and method based on a dynamic weight hybrid model, and belongs to the technical field of time sequence prediction and machine learning. The prediction system comprises a data generation module, a real-time data caching module, a model initialization module, a prediction model selection module, a single model prediction module, a mixed model prediction module, a result display module, an error calculation module, a dynamic weight optimization module and a reset module. According to the method, a dynamic weight fusion strategy is adopted, and contribution weights of ARIMA and LSTM models are dynamically adjusted according to real-time data characteristics (such as data stability, non-linear degree and fluctuation amplitude). A dynamic weight mechanism solves the problem of'one-cut 'of a fixed weight hybrid model, so that the model can maintain the optimal performance in a linear stable scene (such as a new energy output stable time period) and a nonlinear fluctuation scene (such as an extreme weather time period), and the generalization ability of prediction is remarkably improved.

The invention discloses a high-arsenic hot spring replenishment surface water resource quantity determination method, device and equipment, a medium and a product, and relates to the technical field of water resource evaluation and ecological environment protection. The method comprises the following steps: acquiring information data of a target research area; screening symbolic characteristic parameters of the information data by adopting a statistical analysis method and geochemical diagrams to obtain screened information; based on the law of conservation of mass and the end member mixing theory, a multivariate mixing model is constructed according to the screening information; solving the multivariate mixed model by adopting a least square method to obtain a mixing ratio; the mixing ratio is used for analyzing spatial distribution characteristics of the mixing process of the high-arsenic hot spring and the surface water in combination with water flow path survey information; and determining the replenishment resource quantity of the high-arsenic hot spring according to the mixing proportion. The method aims at improving the accuracy and efficiency of determining the surface water resource quantity supplied by the high-arsenic hot spring.

The invention discloses a boiler intelligent combustion accurate air distribution dynamic balance method. The method comprises the steps of multi-dimensional data acquisition, data preprocessing and feature extraction, combustion state intelligent identification, air distribution parameter dynamic optimization and closed-loop control execution. According to the boiler intelligent combustion accurate air distribution dynamic balance method, fuel characteristics, combustion states, flue gas components and air distribution operation full-dimension information are comprehensively captured through a multi-dimension data acquisition system, a scientific feature extraction method is combined to provide a solid basis for combustion state recognition, the combustion type and the unbalance degree are accurately judged by means of a CNN-LSTM mixed model, and the intelligent combustion accurate air distribution dynamic balance method has the advantages of being high in practicability and high in practicability. Then, targeted optimization of the flow, the air speed and the spraying angle of primary air, secondary air and tertiary air is achieved through a dynamic balance algorithm, the problems that ignition is difficult due to insufficient primary air, combustion is insufficient due to uneven distribution of secondary air, and pollution is increased due to excessive tertiary air are solved from the source, air distribution parameters are highly matched with combustion working conditions, and the combustion efficiency is improved. And the combustion process is promoted to tend to an ideal equilibrium state.

The invention relates to the technical field of atmospheric environment measurement and control, and particularly discloses a carbon dioxide column concentration multi-source spatio-temporal data fusion method, which comprises the following steps: by combining a hybrid model of HGT and Transformer, reanalyzing meteorological variables such as modeled XCO2 and wind speed and direction of ERA-5 in a product by utilizing XCO2 and CAMS-IO inverted by an orbital carbon observation satellite OCO-2 satellite, enhancing a CAMS-EGG4 data set, and obtaining a CAMS-EGG4 data set; according to the method, multi-source input features are fused in spatio-temporal joint modeling to realize high precision and strong generalization ability, a heterogeneous spatio-temporal diagram is constructed by combining a spatial proximity relationship with time sequence nodes, so that an internal spatio-temporal dependency structure in atmospheric CO2 observation is explicitly expressed, and the spatial coverage breadth advantage can be maintained while the spatial coverage breadth advantage is maintained. And the observation precision comparable with that of foundation observation is realized.

The invention discloses a special equipment operator electroencephalogram fatigue detection method based on sample compensation and a hybrid model, and the method comprises the steps: collecting an electroencephalogram signal of a special equipment operator, and carrying out the preprocessing of the electroencephalogram signal, and obtaining a preprocessed electroencephalogram signal; performing multi-domain feature extraction and fusion on the preprocessed electroencephalogram signals to obtain a multi-dimensional feature set; performing compensation processing on the electroencephalogram signal and the multi-dimensional feature set by adopting a sample compensation method to obtain a compensated sample feature set; wherein the sample compensation method comprises a characteristic level compensation method and a signal level compensation method; training a hybrid model through the compensated sample feature set to obtain a trained hybrid model; wherein the hybrid model comprises a deep learning model and a traditional machine learning model; and performing electroencephalogram fatigue detection on the special equipment operator through the trained hybrid model to obtain a detection result.

A method and system for anomaly detection from time-series input data. A Gaussian Mixing Model (GMM) learns distribution parameters in an offline learning stage using sample data. The data used for offline learning and for a subsequent online anomaly detection stage are time-series data collected for multiple parameters of a machine operation, such as a robot performing a repetitive set of operations. The method includes aligning the data with a known good reference data file and taking a difference from it before providing the data to the GMM. In an online anomaly detection stage, the GMM calculates a probability that each time-series data point fits the distribution, and then a log-sum calculation is performed on each data file to determine the likelihood that the file contains anomaly data.The log likelihood of the file is compared with previous values, and an alert is issued if there are statistical deviations from the historical data.

The application discloses a sandwich gold purity evaluation method and device based on a mixed model, equipment and a medium. The method comprises the following steps: collecting original data; inputting the processed original data into a mixed model for training to obtain a trained mixed model; wherein the mixed model comprises a feature extraction module, a weighted fusion unit and a classification module connected in sequence; collecting a second pulse eddy current signal and a second supplementary feature corresponding to the to-be-detected gold; inputting the second pulse eddy current signal and the second supplementary feature into the trained mixed model to output a prediction vector; converting the prediction vector into a one-hot code to obtain the number of the to-be-detected gold through the one-hot code. The problem that the existing classification mixed model cannot be applied to the high-fraud gold purity detection scene is solved, the classification accuracy of the fraud gold purity detection is improved, and the application range of the classification mixed model in the rare sample detection field is widened.

The invention discloses an iOS application background task scheduling optimization method and system, and the method comprises the steps: collecting perception data of an accelerometer and an ambient light sensor and network connection mode data, carrying out the analysis and processing of the data through a decision tree and Bayesian network mixed model, and carrying out the recognition of an application scene according to an analysis and processing result; starting a three-dimensional scoring model based on the obtained application scene recognition result, and performing comprehensive value quantification on background tasks in combination with a multi-dimensional dynamic evaluation system; and dynamically allocating CPU and network resources by adopting an elastic time window algorithm according to a comprehensive value quantification result, responding to system resource changes in real time by matching with a lightweight state synchronization mechanism, and scaling background task execution intensity in real time in combination with the residual electric quantity of the equipment and the current temperature state. The method solves the problems that the execution efficiency of the background task of the iOS equipment is low, the system resource distribution is rigid, the adaptability of the existing optimization scheme is poor, and the equipment state perception is insufficient.

The application relates to the computer technical field and discloses a modeling method of a statistical mixed model in a big data distributed scene. The method comprises the following steps: distributing and storing data shards; initializing model parameters; iteratively performing an expectation step and a maximization step; the expectation step is scheduled to a GPU node to perform parallel calculation on posterior probability; the maximization step is scheduled to a CPU node to aggregate statistics and update parameters, perform component merging / deletion, and perform convergence judgment; meanwhile, a memory reuse mechanism based on reference counting and scope analysis is adopted to reduce redundant data transmission. Through heterogeneous task scheduling and memory collaborative optimization, the training speed, resource utilization rate, and model self-adaptation capability are improved.

The invention belongs to the technical field of frequency spectrum prediction, and particularly relates to a frequency spectrum state prediction method based on a hybrid deep learning model, the hybrid deep learning model is fused with a long short-term memory (LSTM) network and a multi-layer perceptron (MLP), and through three-dimensional frequency spectrum data sensing, self-adaptive dual-threshold energy detection and hybrid model prediction, the frequency spectrum state is predicted. And the accuracy of idle channel spectrum prediction is further improved. According to the method, the secondary user (SU) in the cognitive radio system (CRS) can quickly select the channel with the highest idle probability for access, the repeated sensing frequency is reduced, the total sensing time is reduced by 30%, and the effective data transmission time is improved by 30%. Compared with the prior art, the method provided by the invention is higher in frequency spectrum state prediction precision in a low signal-to-noise ratio (SNR) scene, the throughput of the system is remarkably improved, and the energy consumption of the system is lower.

The application discloses a view optimization query method and system for a multi-mode database, and relates to the field of computer view optimization query. A multi-mode JSON view set for a multi-mode database query scene is constructed; a multi-mode database system sequentially passes through a query language analysis module, a query language optimization module and a query language execution module according to the arrival order of a streaming mixed query load Qn to perform data processing; a mixed query request Q1 passes through the query language analysis module to generate a logical execution plan LP1; the LP1 passes through the query language optimization module to generate a physical execution plan PP1; the PP1 passes through the query language execution module to generate a query result D1; until the streaming mixed query load Qn is completely processed, and n query results are returned. The view format of the application can be used for accelerating database query for a single data model, and supports fusion representation of multiple data models, is used for accelerating mixed model query, and solves the problem of model information loss in the multi-mode view.

The invention discloses a carbapenem drug resistance marker screening method and system based on a cross-species compressed Debrueine diagram and a medium. The method comprises the following steps: starting from whole genome sequencing data of gram-negative bacteria belonging to different species and carbapenem drug phenotypes of the gram-negative bacteria, constructing a compressed Debrueine graph based on cross-species joint data, and taking existence / deletion of nodes in the graph as unified genetic variation characteristics. Performing correlation analysis on the nodes and the drug resistance phenotypes by using a linear hybrid model to obtain a candidate node set related to the phenotypes; k-mer is extracted based on the candidate node sequence, and secondary statistical screening is completed in combination with chi-square test and mutual information; and finally determining a group of carbapenem drug-resistant genetic markers which can be applicable across species through a hierarchical feature selection strategy of random forest and XGBoost. Efficient dimension reduction of large-scale cross-species genome data, cross-species consistent variation representation and high-interpretability marker screening are achieved.

The invention relates to a dynamic project cost intelligent prediction method, system and equipment, and the method comprises the steps: anchoring a prediction dimension through a cost motivation map through a closed-loop process of cost motivation quantification, multi-source data structuring, feature engineering and hybrid model dynamic prediction, and carrying out the directional mapping from multi-source data to a cost motivation dimension, performing corresponding feature processing according to feature types on the basis of feature classification defined by the cost motivation quantization atlas to form a structured feature set for model training; project data of different stages are input into a trained mixed cost prediction model, dynamic project cost prediction is achieved, and the mixed cost prediction model comprises an XGBoost model used for extracting static features and an LSTM model based on extracted historical time series data features. Compared with the prior art, the method has the advantages of realizing more accurate and more adaptive project full-stage dynamic cost prediction and the like.

The invention relates to the technical field of ice rink environment control, and particularly provides an ice rink anti-fog prediction method based on machine learning. Comprises: collecting historical environment parameters, and dividing into a training set and a test set; then, a logistic regression model is adopted to quickly calculate the fogging probability, and meanwhile, a GSA-LSTM-Transformer mixed model optimized through a golden sine algorithm is utilized to predict the change trend of environmental parameters; according to the model, the long-term time sequence feature capture capability of the LSTM and the global feature extraction advantage of the Transform are combined; meanwhile, a Levy-GWO optimization algorithm is introduced to carry out dynamic optimization on hyper-parameters of the Stacking integration model; according to the algorithm, the convergence precision and generalization ability of the model are remarkably improved by combining swarm intelligent search of a grey wolf optimization algorithm with a global disturbance strategy of Levy flight; according to the method, through multi-model fusion and intelligent optimization, accurate prediction and dynamic prevention and control of the fogging risk are realized.

The application provides a fresh food transportation whole-process tracking method and system based on RFID. Specifically, a coupling state vector is constructed, which contains a three-dimensional position of a vehicle, a speed, an internal temperature and humidity of goods, and a remaining shelf life of the goods based on an Arrhenius equation; a parallel system model covering possible states of the whole transportation process is constructed; an initial state for the current prediction is generated for each model; for each mixed model, Kalman filtering state prediction is performed, and a process noise covariance matrix Q is dynamically configured according to information including a road type corresponding to a current position of the vehicle and real-time traffic flow; new measurement data is received, effective data is used to update state estimation and likelihood of each model after validity inspection through a verification gate based on Mahalanobis distance, and posterior probability of each model is calculated; and according to the updated model posterior probability, posterior state estimation of all parallel models is weighted and fused to output optimal state estimation at the current time.

The application specifically relates to a passive synthetic aperture sonar direction finding method based on variational message passing, and belongs to the technical field of signal processing. The application adopts a broken line model to fit a distorted array shape, and establishes an array receiving data model containing array shape disturbance, in view of the problems that a traditional algorithm can only process space-time coherent signals, is sensitive to array shape distortion, and has high operation complexity; a spatial domain super-complete dictionary is constructed to convert a direction finding problem into a sparse reconstruction problem, a layered probability model is established by using a Gaussian scale mixed model and is expressed as a factor graph; each parameter posterior probability is solved by using variational message passing iteration, high-dimensional matrix inversion is avoided, and angle fine estimation is realized by combining Laplace interpolation. The application can jointly estimate a signal source direction and array shape distortion parameters, keeps high precision in a random signal and distorted array shape environment, significantly reduces operation complexity, and is suitable for underwater passive synthetic aperture high-resolution direction finding engineering application.

The invention provides an implicit behavior data security acquisition method and device based on federal learning, and relates to the field of data security. The method comprises the following steps: acquiring implicit behavior data in a user equipment side, and preprocessing the implicit behavior data; performing feature extraction operation on the implicit behavior data after the preprocessing operation, and obtaining target behavior features corresponding to the keyboard data, the mouse data and the eye movement data respectively; obtaining hybrid model training parameters based on the target behavior characteristics; training parameters through a hybrid model, and carrying out local model iterative training based on a federated learning dynamic aggregation algorithm; obtaining model parameters after iterative training, and adding encryption noise for the model parameters according to a double-layer differential privacy integration mechanism; and obtaining model parameters after noise addition to complete data security acquisition. The problem that most of applications of existing federal learning in implicit behavior data still stay on the structural level, and the hidden danger of behavior privacy disclosure exists is solved.

The invention discloses a new energy automobile energy consumption prediction method and system based on artificial intelligence, and belongs to the technical field of new energy automobiles, and the method comprises the steps: S1, multi-source dynamic data collection and fusion, S2, data preprocessing and feature engineering, S3, hybrid model architecture design, S4, dynamic environment adaptation and real-time correction, and S5, prediction result output and optimization. On the basis of predicting the energy consumption of the new energy automobile, the system energy consumption can be assisted to be incorporated into the model, and the method can adapt to a dynamic environment.

The invention provides a textbook data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an original textbook data file; extracting a data type of the original textbook data file, and analyzing the original textbook data file according to the data type to obtain structured content data; inputting the structured content data and the corresponding data type into a preset multi-data type hybrid model for processing, and obtaining processed result data and the data type corresponding to the structured content data; wherein the multi-modal data set is used for generating a multi-modal sample set for training the corresponding wisdom education large model according to the training demand of the wisdom education large model. According to the method, the efficiency and accuracy of constructing the multi-modal data set can be improved.

The invention discloses an LNG low-temperature test center abnormal sound recognition method based on recursive mixed learning, and the method specifically comprises the following steps: S1, audio data preprocessing: carrying out the preprocessing of an audio signal collected from an LNG low-temperature test center, and carrying out the data standardization, band-pass filtering and denoising processing of the audio data; s2, audio data feature extraction: performing feature extraction on the preprocessed audio data through an auto-encoder, and extracting a low-dimensional and high-information-density feature vector from the preprocessed audio data; and S3, constructing a self-adaptive recursive hybrid model based on the recursive hybrid learning framework, and performing abnormal sound recognition based on the self-adaptive recursive hybrid model. The model constructed by the invention can intelligently distribute less calculation for simple background noise, and more calculation amount is concentrated on complex sound features which may represent abnormities, so that the overall calculation efficiency of the model is greatly improved on the premise that the recognition accuracy is not sacrificed.

A system and method to detect and reduce noise include receiving a request and deploying a linear mixed model in a plurality of iterations to determine, based on one or more parameters of the linear mixed model, that noise is present in an output of the linear mixed model, determine that the noise is not larger than a threshold and that the output is capable of further improvement, adjust at least some of the one or more parameters to obtain one or more adjusted parameters, adjust the output of the linear mixed model based on the one or more adjusted parameters to reduce the noise in the output, determine whether a stopping criterion is reached, and responsive to determining that the stopping criterion has not reached, repeat next iteration with the adjusted output or responsive to determining that the stopping criterion has reached, transmit the output.

The application discloses an enhanced multi-linear mixed hyperspectral unmixing method, system and device, which comprises the following steps: acquiring hyperspectral image data of a region to be processed, and preprocessing the hyperspectral image data; pre-extracting endmember and abundance information from the preprocessed hyperspectral image data; constructing a multi-branch variational autoencoder unmixing network; based on an enhanced multi-linear mixed model considering spectral variability, performing reconstruction calculation on a pixel at the end of the multi-branch variational autoencoder unmixing network; constructing a joint loss function, and iteratively optimizing and training the multi-branch variational autoencoder unmixing network; and outputting an unmixing result, and obtaining a variable endmember spectrum and a corresponding ground object abundance distribution. The application combines the feature representation capability of a deep generative model and the mechanism driving of a real physical model, and can still maintain high endmember extraction and abundance inversion precision under the coupling of spectral variability and nonlinear spectral mixing and complex interference, and the performance of hyperspectral unmixing in a complex environment is improved.

The invention discloses a method for evaluating the corrosion fatigue crack growth rate of a steel member by fusing a physical model and data driving. The method comprises the following steps: firstly, acquiring data through pre-corrosion and fatigue tests, and calculating a stress intensity factor by adopting M-integral; and further, analyzing the characteristic correlation by using a Pearson coefficient and an SHAP-XGBoost method and quantifying the contribution degree of each parameter to crack propagation, thereby proposing a Paris correction formula. Then, a mixed loss function is constructed, data-driven artificial neural network loss and physical model loss based on a Paris correction formula are considered at the same time, and the data-driven artificial neural network loss and the physical model loss are harmonized through a physical influence coefficient; and finally, performing cycle training and hyper-parameter optimization on the mixed model by adopting a Bayesian optimizer until the prediction precision reaches a set threshold value, thereby realizing high-precision crack growth rate prediction. The prediction method has high precision and high reliability, and can be suitable for crack propagation prediction of actual engineering steel structures such as bridges, civil engineering and ocean engineering.

The application discloses a low-altitude airspace multi-source data fusion congestion prediction and dynamic scheduling system, which solves the problems of incomplete data fusion, low congestion prediction accuracy, lack of coordination and real-time in existing low-altitude airspace traffic management. The system includes a perception layer, a data fusion layer, a prediction layer, a decision-making and scheduling layer, a control execution layer, and a blockchain storage layer. The perception layer collects multi-source data, the data fusion layer realizes data fusion through an improved deep belief network, the prediction layer adopts a mixed model of LSTM and graph neural network to output congestion prediction results, the decision-making and scheduling layer generates a dynamic scheduling scheme based on a multi-objective optimization algorithm, the control execution layer executes the scheme and feeds back data, and the blockchain storage layer guarantees data security and privacy. The application improves the accuracy of congestion prediction and the adaptability of the scheduling scheme, and realizes efficient, safe and coordinated operation of the low-altitude airspace.

The application discloses a kind of based on machine learning's main girder erects elevation adjustment method, belong to elevation adjustment technical field, this method constructs space-time database by gathering the data of no cable section construction period;Adopt the mixed model of LSTM-Transformer to process time series data, extract long-period characteristic, output initial predicted value of elevation;Nonlinear variable such as the change rate of cable force, sunshine gradient and material age is analyzed using XG-Boost algorithm, and compensation factor is output;Fusion and generate joint predicted value;Construct reinforcement learning intelligent agent, with construction stage as state space, elevation adjustment quantity as action space, the deviation of predicted value and measured value and construction stability are minimized as reward function, and output adjustment instruction;According to driving hydraulic system, adjust erecting elevation, and real-time update database.The application improves the precision and stability of elevation adjustment by multi-dimensional data fusion and intelligent optimization.